File size: 10,681 Bytes
0a6581c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
{
"results": {
"hellaswag": {
"acc,none": 0.6730730930093607,
"acc_stderr,none": 0.0046813160644444095,
"acc_norm,none": 0.8536148177653854,
"acc_norm_stderr,none": 0.0035276951498235012,
"alias": "hellaswag"
},
"eq_bench": {
"eqbench,none": 74.82935321697907,
"eqbench_stderr,none": 1.6591997145588517,
"percent_parseable,none": 100.0,
"percent_parseable_stderr,none": 0.0,
"alias": "eq_bench"
}
},
"group_subtasks": {
"eq_bench": [],
"hellaswag": []
},
"configs": {
"eq_bench": {
"task": "eq_bench",
"dataset_path": "pbevan11/EQ-Bench",
"validation_split": "validation",
"doc_to_text": "prompt",
"doc_to_target": "reference_answer_fullscale",
"process_results": "def calculate_score_fullscale(docs, results):\n reference = eval(docs[\"reference_answer_fullscale\"])\n user = dict(re.findall(r\"(\\w+):\\s+(\\d+)\", results[0]))\n # First check that the emotions specified in the answer match those in the reference\n if len(user.items()) != 4:\n # print('! Error: 4 emotions were not returned')\n # print(user)\n return {\"eqbench\": 0, \"percent_parseable\": 0}\n emotions_dict = {}\n for emotion, user_emotion_score in user.items():\n for i in range(1, 5):\n if emotion == reference[f\"emotion{i}\"]:\n emotions_dict[emotion] = True\n if len(emotions_dict) != 4:\n print(\"! Error: emotions did not match reference\")\n print(user)\n return {\"eqbench\": 0, \"percent_parseable\": 0}\n\n difference_tally = (\n 0 # Tally of differerence from reference answers for this question\n )\n\n # Iterate over each emotion in the user's answers.\n for emotion, user_emotion_score in user.items():\n # If this emotion is in the reference, calculate the difference between the user's score and the reference score.\n for i in range(1, 5):\n if emotion == reference[f\"emotion{i}\"]:\n d = abs(\n float(user_emotion_score) - float(reference[f\"emotion{i}_score\"])\n )\n # this will be a value between 0 and 10\n if d == 0:\n scaled_difference = 0\n elif d <= 5:\n # S-shaped scaling function\n # https://www.desmos.com/calculator\n # 6.5\\cdot\\ \\frac{1}{\\left(1\\ +\\ e^{\\left(-1.2\\cdot\\left(x-4\\right)\\right)}\\right)}\n scaled_difference = 6.5 * (1 / (1 + math.e ** (-1.2 * (d - 4))))\n\n else:\n scaled_difference = d\n difference_tally += scaled_difference\n\n # Inverting the difference tally so that the closer the answer is to reference, the higher the score.\n # The adjustment constant is chosen such that answering randomly produces a score of zero.\n adjust_const = 0.7477\n final_score = 10 - (difference_tally * adjust_const)\n final_score_percent = final_score * 10\n\n return {\"eqbench\": final_score_percent, \"percent_parseable\": 100}\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "eqbench",
"aggregation": "mean",
"higher_is_better": true
},
{
"metric": "percent_parseable",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"do_sample": false,
"temperature": 0.0,
"max_gen_toks": 80,
"until": [
"\n\n"
]
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 2.1
}
},
"hellaswag": {
"task": "hellaswag",
"group": [
"multiple_choice"
],
"dataset_path": "hellaswag",
"training_split": "train",
"validation_split": "validation",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "{{query}}",
"doc_to_target": "{{label}}",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
},
{
"metric": "acc_norm",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
}
},
"versions": {
"eq_bench": 2.1,
"hellaswag": 1.0
},
"n-shot": {
"eq_bench": 0,
"hellaswag": 0
},
"higher_is_better": {
"eq_bench": {
"eqbench": true,
"percent_parseable": true
},
"hellaswag": {
"acc": true,
"acc_norm": true
}
},
"n-samples": {
"hellaswag": {
"original": 10042,
"effective": 10042
},
"eq_bench": {
"original": 171,
"effective": 171
}
},
"config": {
"model": "hf",
"model_args": "pretrained=athirdpath/NSFW_DPO_vmgb-7b,trust_remote_code=True",
"model_num_parameters": 7241732096,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "b667d7810267685cd4f32a8f82044e419c010abe",
"batch_size": "auto",
"batch_sizes": [
64
],
"device": null,
"use_cache": null,
"limit": null,
"bootstrap_iters": 100000,
"gen_kwargs": null,
"random_seed": 0,
"numpy_seed": 1234,
"torch_seed": 1234,
"fewshot_seed": 1234
},
"git_hash": null,
"date": 1719541817.2830012,
"pretty_env_info": "PyTorch version: 2.3.1+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.4 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-6.5.0-1022-gcp-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.1.105\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA L4\nGPU 1: NVIDIA L4\n\nNvidia driver version: 555.42.02\ncuDNN version: Could not collect\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 24\nOn-line CPU(s) list: 0-23\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) CPU @ 2.20GHz\nCPU family: 6\nModel: 85\nThread(s) per core: 2\nCore(s) per socket: 12\nSocket(s): 1\nStepping: 7\nBogoMIPS: 4400.47\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat avx512_vnni md_clear arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 384 KiB (12 instances)\nL1i cache: 384 KiB (12 instances)\nL2 cache: 12 MiB (12 instances)\nL3 cache: 38.5 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-23\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Mitigation; Clear CPU buffers; SMT Host state unknown\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown\nVulnerability Retbleed: Mitigation; Enhanced IBRS\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI Syscall hardening, KVM SW loop\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT Host state unknown\n\nVersions of relevant libraries:\n[pip3] numpy==2.0.0\n[pip3] torch==2.3.1\n[pip3] triton==2.3.1\n[conda] Could not collect",
"transformers_version": "4.41.2",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<unk>",
0
],
"tokenizer_eos_token": [
"</s>",
2
],
"tokenizer_bos_token": [
"<s>",
1
],
"eot_token_id": 2,
"max_length": 32768,
"task_hashes": {},
"model_source": "hf",
"model_name": "athirdpath/NSFW_DPO_vmgb-7b",
"model_name_sanitized": "athirdpath__NSFW_DPO_vmgb-7b",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 94200.513127129,
"end_time": 95702.225070561,
"total_evaluation_time_seconds": "1501.7119434320048"
} |